A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the re...A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.展开更多
The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig a...The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig area was examined using the images of 47 pigs of different weights.Relationship between the threshold value and the extracted area was numerically analyzed for both methods.It was found that the accuracy of the conventional method depended heavily on the threshold value,while choice of threshold value in the edge detection approach had no influence on the extracted area over a wide range.In normal lighting conditions,both methods yielded comparable values of predicted weight;however,under variable light intensities,the edge detection method was superior to the conventional method,because the former was proven to be independent of light intensities.This makes edge detection an ideal method for area extraction during the walk-through weighing process where pigs are allowed to move around.展开更多
Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losse...Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losses.Image-processing algorithms were developed for detecting and differentiating between three Cadra egg categories based on the success of Trichogramma bourarachae(Pintureau and Babaul)parasitization.These categories were parasitized(black and dark red),unparasitized fertile unhatched(yellow),and unparasitized hatched(white)eggs.Color,light intensity,and shape information was used to develop detection algorithms.Two image processing methods were developed based on three randomly selected images and were tested on a larger validation image set of 40 images:(i)segmentation and extractions of color and morphological features followed by Watershed delineation,and is referred to as Algorithm 1(ALGO1),(ii)finding circular objects by Hough Transformation followed by convolution filtering,and is referred to as Algorithm 2(ALGO2).ALGO1 and ALGO2 achieved correct classification rates(CCRs)for parasitized eggs of 92%and 96%,respectively.Their CCRs for unhatched eggs were 48%and 94%,and for hatched eggs were 42%and 73%,respectively.Regarding parasitized eggs,both methods performed satisfactorily,but,in general,ALGO2 outperformed ALGO1.These results ensure automatic evaluation of the efficiency of biological control of Cadra cautella by the egg parasitoid Trichogramma bourarachae by quantifying the rate of parasitization.The developed detection methods can be used by producers of biocontrol agents for online monitoring of Trichogramma and similar insect natural enemies during mass production and before release against crop pests.Moreover,with few adjustments these methods can be used in similar applications such as detecting plant diseases.展开更多
The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be...The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be done:the target area of the binary image was determined by mathematical morphology and removal of the object of a small area.According to the binary image is a convex or concave figure,the target region light leaked or not was determined.The effects of leaked region were eliminated by searching for mutation points,fitting salted egg boundary by the Least Square algorithm,labeling the binary image and extracting single target area.Then,six characteristic parameters were extracted in color space,and quality testing model was established by minimum error probability.The experimental results indicated that the detection accuracy reached above 93%and classification efficiency was 5400/h.It is proved the model is feasible for salted egg grading.展开更多
基金Supported by Henan Institute of Science and Technology (055031)
文摘A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.
基金The project was supported in part by the National Research Initiative of the USDA Cooperative State Research,Education and Extension Service,grant number 2003-35503-13990.
文摘The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig area was examined using the images of 47 pigs of different weights.Relationship between the threshold value and the extracted area was numerically analyzed for both methods.It was found that the accuracy of the conventional method depended heavily on the threshold value,while choice of threshold value in the edge detection approach had no influence on the extracted area over a wide range.In normal lighting conditions,both methods yielded comparable values of predicted weight;however,under variable light intensities,the edge detection method was superior to the conventional method,because the former was proven to be independent of light intensities.This makes edge detection an ideal method for area extraction during the walk-through weighing process where pigs are allowed to move around.
文摘Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losses.Image-processing algorithms were developed for detecting and differentiating between three Cadra egg categories based on the success of Trichogramma bourarachae(Pintureau and Babaul)parasitization.These categories were parasitized(black and dark red),unparasitized fertile unhatched(yellow),and unparasitized hatched(white)eggs.Color,light intensity,and shape information was used to develop detection algorithms.Two image processing methods were developed based on three randomly selected images and were tested on a larger validation image set of 40 images:(i)segmentation and extractions of color and morphological features followed by Watershed delineation,and is referred to as Algorithm 1(ALGO1),(ii)finding circular objects by Hough Transformation followed by convolution filtering,and is referred to as Algorithm 2(ALGO2).ALGO1 and ALGO2 achieved correct classification rates(CCRs)for parasitized eggs of 92%and 96%,respectively.Their CCRs for unhatched eggs were 48%and 94%,and for hatched eggs were 42%and 73%,respectively.Regarding parasitized eggs,both methods performed satisfactorily,but,in general,ALGO2 outperformed ALGO1.These results ensure automatic evaluation of the efficiency of biological control of Cadra cautella by the egg parasitoid Trichogramma bourarachae by quantifying the rate of parasitization.The developed detection methods can be used by producers of biocontrol agents for online monitoring of Trichogramma and similar insect natural enemies during mass production and before release against crop pests.Moreover,with few adjustments these methods can be used in similar applications such as detecting plant diseases.
基金supported by National Natural Science Foundation of China(31371771)Special Fund for Agro-scientific Research in the Public Interest(201303084)National Key Technology Research and Development Program Project(2015BAD19B05).
文摘The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be done:the target area of the binary image was determined by mathematical morphology and removal of the object of a small area.According to the binary image is a convex or concave figure,the target region light leaked or not was determined.The effects of leaked region were eliminated by searching for mutation points,fitting salted egg boundary by the Least Square algorithm,labeling the binary image and extracting single target area.Then,six characteristic parameters were extracted in color space,and quality testing model was established by minimum error probability.The experimental results indicated that the detection accuracy reached above 93%and classification efficiency was 5400/h.It is proved the model is feasible for salted egg grading.
